Advancing Pediatric Brain Research with Privacy-Protecting AI
The American Journal of Neuroradiology (AJNR) recently published groundbreaking work by Dr. Ariana Familiar, a senior data scientist and team lead, and Dr. Ali Nabavizadeh, a neuroradiologist and director of the imaging unit at the Center for Data-Driven Discovery in Biomedicine (D3b). This article highlights a significant advancement in pediatric brain tumor research through the development of an innovative AI tool designed to protect children’s data.
This research addresses the critical issue of data privacy in pediatric neuroimaging. Brain MRI scans contain identifiable facial information, making data sharing challenging due to privacy concerns. To tackle this, Familiar and her team developed a pediatric auto-defacing tool. This AI-driven solution automatically removes facial features from brain MRIs, enabling safer data sharing and advancing neuroscience research.
This de-identification tool is an essential scientific breakthrough that provides a robust method for de-identifying pediatric brain images. The AI technology was meticulously developed using the Children’s Brain Tumor Network (CBTN) dataset and other control data to ensure accuracy and applicability across diverse pediatric cases and image types.
Back to the Beginning
Ariana Familiar’s journey to this pivotal achievement is rooted in her extensive background in pediatric brain tumor research and data science. With a BA/BAS in psychology and philosophy from New York University and a PhD from the University of Pennsylvania, her expertise spans cognitive neuroscience, visual memory, and social behavior. Familiar’s passion for pediatric cancer research is evident in her leadership at D3b, where she spearheads efforts in multi-site data sharing, data aggregation, and the development of analytical methods for radiology data.
“The intersection of healthcare and technology was super interesting to me,” Familiar notes, highlighting her motivation to focus on this critical area of research.
The Science of Ethical Research
Developing the “pediatric-auto-defacer” underscores D3b’s commitment to ethical research practices. This work exemplifies the center’s dedication to advancing pediatric brain tumor research while upholding the highest ethical standards by prioritizing patient privacy and developing a tool that enables responsible data sharing.
Impact on Pediatric Research
The development of this AI tool has profound implications for pediatric research. It promotes enhanced data sharing and discovery, accelerates the development of improved treatment options, and ultimately leads to better outcomes for children with cancer. By safeguarding children’s identities, this tool empowers researchers, clinicians, and families to contribute to and benefit from collaborative research efforts.
The Future Begins with AI
This innovative AI tool and Familiar’s publication in the AJNR spotlight a significant leap forward in pediatric brain research. By prioritizing data privacy, this work underscores the commitment of CBTN and D3b to responsibly advancing the field and improving the lives of children and their families. We look forward to the future discoveries this advancement will enable and its positive impact on pediatric healthcare.
To read more about the pediatric-auto-defacer and what it means to pediatric research, visit the American Journal of Neuroradiology.